def __import_time_series_csv_datatype(self, hrf_folder, connectivity_gid,
patient, user_tag):
path = os.path.join(hrf_folder, self.TIME_SERIES_CSV_FILE)
with open(path) as csv_file:
csv_reader = csv.reader(
csv_file, delimiter=CSVDelimiterOptionsEnum.COMMA.value)
ts = list(csv_reader)
ts_data = np.array(ts, dtype=np.float64).reshape(
(len(ts), 1, len(ts[0]), 1))
ts_time = np.random.rand(ts_data.shape[0], )
project = dao.get_project_by_id(self.current_project_id)
ts_gid = uuid.uuid4()
h5_path = "TimeSeries_{}.h5".format(ts_gid.hex)
operation_folder = self.storage_interface.get_project_folder(
project.name, str(self.operation_id))
h5_path = os.path.join(operation_folder, h5_path)
conn = h5.load_from_gid(connectivity_gid)
ts = TimeSeriesRegion()
ts.data = ts_data
ts.time = ts_time
ts.gid = ts_gid
ts.connectivity = conn
generic_attributes = GenericAttributes()
generic_attributes.user_tag_1 = user_tag
generic_attributes.state = DEFAULTDATASTATE_RAW_DATA
with TimeSeriesRegionH5(h5_path) as ts_h5:
ts_h5.store(ts)
ts_h5.nr_dimensions.store(4)
ts_h5.subject.store(patient)
ts_h5.store_generic_attributes(generic_attributes)
ts_index = TimeSeriesIndex()
ts_index.gid = ts_gid.hex
ts_index.fk_from_operation = self.operation_id
ts_index.time_series_type = "TimeSeriesRegion"
ts_index.data_length_1d = ts_data.shape[0]
ts_index.data_length_2d = ts_data.shape[1]
ts_index.data_length_3d = ts_data.shape[2]
ts_index.data_length_4d = ts_data.shape[3]
ts_index.data_ndim = len(ts_data.shape)
ts_index.sample_period_unit = 'ms'
ts_index.sample_period = TimeSeries.sample_period.default
ts_index.sample_rate = 1024.0
ts_index.subject = patient
ts_index.state = DEFAULTDATASTATE_RAW_DATA
ts_index.labels_ordering = json.dumps(
list(TimeSeries.labels_ordering.default))
ts_index.labels_dimensions = json.dumps(
TimeSeries.labels_dimensions.default)
ts_index.visible = False # we don't want to show these TimeSeries because they are dummy
dao.store_entity(ts_index)
return ts_gid
def test_adapter_launch(self):
"""
Test that the adapters launches and successfully generates a datatype measure entry.
"""
meta = {
DataTypeMetaData.KEY_SUBJECT: "John Doe",
DataTypeMetaData.KEY_STATE: "RAW_DATA"
}
algo_group = FlowService().get_algorithm_by_module_and_class(
SIMULATOR_MODULE, SIMULATOR_CLASS)[1]
self.operation = model.Operation(self.test_user.id,
self.test_project.id,
algo_group.id,
json.dumps(''),
meta=json.dumps(meta),
status=model.STATUS_STARTED,
method_name=ABCAdapter.LAUNCH_METHOD)
self.operation = dao.store_entity(self.operation)
storage_path = FilesHelper().get_project_folder(
self.test_project, str(self.operation.id))
dummy_input = numpy.arange(1, 10001).reshape(10, 10, 10, 10)
dummy_time = numpy.arange(1, 11)
# Get connectivity
connectivities = FlowService().get_available_datatypes(
self.test_project.id, "tvb.datatypes.connectivity.Connectivity")[0]
self.assertEqual(2, len(connectivities))
connectivity_gid = connectivities[0][2]
dummy_time_series = TimeSeriesRegion()
dummy_time_series.storage_path = storage_path
dummy_time_series.write_data_slice(dummy_input)
dummy_time_series.write_time_slice(dummy_time)
dummy_time_series.close_file()
dummy_time_series.start_time = 0.0
dummy_time_series.sample_period = 1.0
dummy_time_series.connectivity = connectivity_gid
adapter_instance = StoreAdapter([dummy_time_series])
OperationService().initiate_prelaunch(self.operation, adapter_instance,
{})
dummy_time_series = dao.get_generic_entity(dummy_time_series.__class__,
dummy_time_series.gid,
'gid')[0]
ts_metric_adapter = TimeseriesMetricsAdapter()
resulted_metric = ts_metric_adapter.launch(dummy_time_series)
self.assertTrue(isinstance(resulted_metric, DatatypeMeasure),
"Result should be a datatype measure.")
self.assertTrue(
len(resulted_metric.metrics) >= len(
ts_metric_adapter.available_algorithms.keys()),
"At least a result should have been generated for every metric.")
for metric_value in resulted_metric.metrics.values():
self.assertTrue(isinstance(metric_value, (float, int)))
def test_adapter_launch(self):
"""
Test that the adapters launches and successfully generates a datatype measure entry.
"""
meta = {DataTypeMetaData.KEY_SUBJECT: "John Doe", DataTypeMetaData.KEY_STATE: "RAW_DATA"}
algo_group = FlowService().get_algorithm_by_module_and_class(SIMULATOR_MODULE, SIMULATOR_CLASS)[1]
self.operation = model.Operation(self.test_user.id, self.test_project.id, algo_group.id, json.dumps(''),
meta=json.dumps(meta), status=model.STATUS_STARTED,
method_name=ABCAdapter.LAUNCH_METHOD)
self.operation = dao.store_entity(self.operation)
storage_path = FilesHelper().get_project_folder(self.test_project, str(self.operation.id))
dummy_input = numpy.arange(1, 10001).reshape(10, 10, 10, 10)
dummy_time = numpy.arange(1, 11)
# Get connectivity
connectivities = FlowService().get_available_datatypes(self.test_project.id,
"tvb.datatypes.connectivity.Connectivity")[0]
self.assertEqual(2, len(connectivities))
connectivity_gid = connectivities[0][2]
dummy_time_series = TimeSeriesRegion()
dummy_time_series.storage_path = storage_path
dummy_time_series.write_data_slice(dummy_input)
dummy_time_series.write_time_slice(dummy_time)
dummy_time_series.close_file()
dummy_time_series.start_time = 0.0
dummy_time_series.sample_period = 1.0
dummy_time_series.connectivity = connectivity_gid
adapter_instance = StoreAdapter([dummy_time_series])
OperationService().initiate_prelaunch(self.operation, adapter_instance, {})
dummy_time_series = dao.get_generic_entity(dummy_time_series.__class__, dummy_time_series.gid, 'gid')[0]
ts_metric_adapter = TimeseriesMetricsAdapter()
resulted_metric = ts_metric_adapter.launch(dummy_time_series)
self.assertTrue(isinstance(resulted_metric, DatatypeMeasure), "Result should be a datatype measure.")
self.assertTrue(len(resulted_metric.metrics) >= len(ts_metric_adapter.available_algorithms.keys()),
"At least a result should have been generated for every metric.")
for metric_value in resulted_metric.metrics.values():
self.assertTrue(isinstance(metric_value, (float, int)))
